Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis

• URL: http://arxiv.org/abs/2401.05453v2
• Date: Sun, 21 Apr 2024 03:57:31 GMT
• Title: Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis
• Authors: Alastair Anderberg, James Bailey, Ricardo J. G. B. Campello, Michael E. Houle, Henrique O. Marques, Miloš Radovanović, Arthur Zimek,
• Abstract summary: We present a nonparametric method for outlier detection that takes full account of local variations in dimensionality within the dataset. We show that significantly outperforms three popular and important benchmark outlier detection methods.
• Score: 9.962838991341874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a nonparametric method for outlier detection that takes full account of local variations in intrinsic dimensionality within the dataset. Using the theory of Local Intrinsic Dimensionality (LID), our 'dimensionality-aware' outlier detection method, DAO, is derived as an estimator of an asymptotic local expected density ratio involving the query point and a close neighbor drawn at random. The dimensionality-aware behavior of DAO is due to its use of local estimation of LID values in a theoretically-justified way. Through comprehensive experimentation on more than 800 synthetic and real datasets, we show that DAO significantly outperforms three popular and important benchmark outlier detection methods: Local Outlier Factor (LOF), Simplified LOF, and kNN.

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